New opportunities and challenges for conservation evidence synthesis from advances in natural language processing DOI Creative Commons
Charlotte H. Chang, Susan C. Cook‐Patton, James T. Erbaugh

и другие.

Conservation Biology, Год журнала: 2025, Номер 39(2)

Опубликована: Апрель 1, 2025

Abstract Addressing global environmental conservation problems requires rapidly translating natural and social science evidence to policy‐relevant information. Yet, exponential increases in scientific production combined with disciplinary differences reporting research make interdisciplinary syntheses especially challenging. Ongoing developments language processing (NLP), such as large models, machine learning (ML), data mining, hold the promise of accelerating cross‐disciplinary primary research. The evolution ML, NLP, artificial intelligence (AI) systems computational provides new approaches accelerate all stages synthesis science. To show how processing, AI can help automate scale science, we describe methods that querying literature, process unstructured bodies textual evidence, extract parameters interest from studies. Automation translate other agendas by categorizing labeling at scale, yet there are major unanswered questions about use hybrid AI‐expert ethically effectively conservation.

Язык: Английский

Governance and resilience as entry points for transforming food systems in the countdown to 2030 DOI Creative Commons
Kate Schneider, Roseline Remans, Tesfaye Hailu Bekele

и другие.

Nature Food, Год журнала: 2025, Номер unknown

Опубликована: Янв. 14, 2025

Due to complex interactions, changes in any one area of food systems are likely impact-and possibly depend on-changes other areas. Here we present the first annual monitoring update indicator framework proposed by Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, find that 20 42 indicators time series have been trending a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience be most connected themes, highlighting entry points for action-particularly action. Literature review country case studies add context diets, environment, livelihoods, indicators, helping different actors understand navigate towards

Язык: Английский

Процитировано

0
New opportunities and challenges for conservation evidence synthesis from advances in natural language processing DOI Creative Commons
Charlotte H. Chang, Susan C. Cook‐Patton, James T. Erbaugh

и другие.

Conservation Biology, Год журнала: 2025, Номер 39(2)

Опубликована: Апрель 1, 2025

Abstract Addressing global environmental conservation problems requires rapidly translating natural and social science evidence to policy‐relevant information. Yet, exponential increases in scientific production combined with disciplinary differences reporting research make interdisciplinary syntheses especially challenging. Ongoing developments language processing (NLP), such as large models, machine learning (ML), data mining, hold the promise of accelerating cross‐disciplinary primary research. The evolution ML, NLP, artificial intelligence (AI) systems computational provides new approaches accelerate all stages synthesis science. To show how processing, AI can help automate scale science, we describe methods that querying literature, process unstructured bodies textual evidence, extract parameters interest from studies. Automation translate other agendas by categorizing labeling at scale, yet there are major unanswered questions about use hybrid AI‐expert ethically effectively conservation.

Язык: Английский

Процитировано

0